Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evaluated. In this study, besides the comparison of simple and noisy Independent Component Analysis (ICA) algorithms, the quantity effects of some spatial and temporal filtering have been evaluated on the functionality of ICA algorithms. Noisy ICA algorithms perform with a higher accuracy (up to 16%) in comparison to simple ICA for noisy fMRI data, although it is more time consuming than simple ICA. The accuracy of the results is improved by 8-10% using spatial and temporal filtering prior to simple ICA. Materials and Methods: Simple ICA and noisy ICA methods have been compared for analyzing simulated fMRI data sets. The impact of some temporal and spatial filters on the functionality of simple ICA algorithms has been evaluated. Implemented filters have been proposed in low and high pass group. Results: The sensitivity, specificity and temporal accuracy of simple ICA algorithms has been improved by using high pass filters. Although low pass filtering has some positive effects on the performance of simple ICA algorithms in the low SNR levels, in the high signal-noise Ratio (SNR) levels these low pass filters may cause a decrease in the sensitivity, specificity and temporal accuracy of simple ICA methods. Discussion and Conclusion: The results obtained from simple and noisy ICA algorithms for analyzing fMRI data having high SNR levels are approximately similar. Infomax algorithm uses Gradient based methods for estimating unmixing matrix has better sensitivity, specificity and temporal accuracy than Fast ICA for analyzing noisy ICA data. An alternative to the complicated and time consuming noisy ICA algorithms is to preprocess and denoise fMRI data prior to analyzing it by simple ICA algorithms.

Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis. Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models (GDM) in combination with an advanced region growing and thresholding methods is proposed. GDM are found to be an attractive tool for structural based image segmentation particularly for extracting the objects with complicated topology. There are two main parameters influencing the overall performance of GDM algorithm; the distance between the initial contour and the actual object’s contours and secondly the stopping term which controls the deformation. To overcome these limitations, a two stage hybrid based segmentation method is suggested to extract the rough but precise initial contours at the first stage of the segmentation. The extracted boundaries are smoothed and improved using a modified GDM algorithm by improving the stopping terms of the algorithm based on the gradient value of image voxels. Results: The proposed algorithm was implemented on forty data sets each containing 400-480 slices. The results show an improvement in the accuracy and smoothness of the extracted boundaries. The improvement obtained for the accuracy of segmentation is about 6% in comparison to the one achieved by the methods based on thresholding and region growing only. Discussion and Conclusion: The extracted contours using modified GDM are smoother and finer. The improvement achieved in this work on the performance of stopping function of GDM model together with applying two stage segmentation of boundaries have resulted in a great improvement on the computational efficiency of GDM algorithm while making smoother and finer colon borders.

Introduction: contrast sensitivity is one of the most important psychophysical tests that may be used for the evaluation of refractive state of eye and retinal image quality. Yellow vs. blue color contrast sensitivity may be more suitable in this regard. Materials and Methods: thirty myopic eyes were considered in this study. Yellow vs. blue color contrast sensitivity was evaluated under the same environment and conditions in these individuals pre and post LASIK. Results: The comparison of yellow vs. blue color contrast sensitivity with glasses pr e and post LASIK shows a significant improvement (p<0.001) in the contrast sensitivity. Discussion and Conclusion: The improvement of optical state of the eye is the main reason for the improvement of contrast sensitivity. The reason behind performing LASIK surgery and surgeon's experience on one hand, and the undesirable quality of glasses due to the lack of standard in the manufacturing and glazing of correcting lenses on the other hand are the main factors behind the improvement of optical state of the eye.

Introduction: The medical applications of ultrasound on human brain are highly limited by the phase and amplitude aberrations induced by the heterogeneities of the skull. However, it has been shown that time reversing coupled with amplitude compensation can overcome these aberrations. In this work, a model for 2D simulation of the time reversal mirror technique is proposed to study the possibility of targeting any point within the brain without the need for craniotomy and to calculate the acoustic pressure field and the resulting temperature distribution within the skull and brain during a High Intensity Focused Ultrasound (HIFU) transcranial therapy. Materials and Methods: To overcome the sensitivity of the wave pattern to the heterogeneous geometry of the skull, a real MRI derived 2D model is constructed. The model should include the real geometry of brain and skull. The model should also include the couplant medium which has the responsibility of coupling the transducer to the skull for the penetration of ultrasound. The clinical substance used as the couplant is water. The acoustic and thermal parameters are derived from the references. Next, the wave propagation through the skull is computed based on the Helmholtz equation, with a 2D finite element analysis. The acoustic simulation is combined with a 2D thermal diffusion analysis based on Pennes Bioheat equation and the temperature elevation inside the skull and brain is computed. The numerical simulations were performed using the FEMLAB 3.2 software on a PC having 8 GB RAM and a 2.4 MHz dual CPU. Results: It is seen that the ultrasonic waves are exactly focalized at the location where the hydrophone has been previously implanted. There is no penetration into the sinuses and the waves are reflected from their surface because of the high discrepancy between the speed of sound in bone and air. Under the focal pressure of 2.5 MPa and after 4 seconds of sonication the temperature at the focus reached 51 °C and the temperature of the pre-target bone increased to 56.31 °C. In the post-target region the temperature of the sphenoid bone increased to 47.1 °C while the temperature of the occipital bones reached up to 46 °C. It is also shown that by using a cold water cooling system and cooling down the pre-target bones to 20 °C before sonication, the maximum pre-target bone temperature will not exceed 40 °C and hence the pre-target bone cells will remain intact. Discussion and Conclusion: In this study, it is well demonstrated that by using the time reversal mirror technique it is possible to target any point within the skull without the need for craniotomy. Although at higher acoustic frequencies compared to the lower ones such as 300 kHz the ultrasound undergoes more severe aberrations while passing through media having geometrical heterogeneity and discrepant sound velocities, the simulations performed in this work show that even at such frequencies it is still possible to correct these aberrations using the time reversal mirror technique. The thermal simulations show that by using this method the temperature of the deep seated tumors can be increased to cytotoxic temperature in a few seconds.

Introduction: One of the most common syndromes in Parkinson's disease (PD) is rigidity. Currently, an index is used to evaluate the level of PD by the clinical measurement of rigidity in the upper extremity. The index uses a subjective method called Unified Parkinson's Disease Rating System (UPDRS). The subjective nature of this method makes the influence of physician in the measurement of rigidity possible. Hence, the development of a new standard method based on objective indices is needed. Materials and Methods: In this research, a new device was fabricated and used to measure the viscous and elastic indices and the range of motion during passive movement of elbow joint. The relation between each index and the level of illness was analyzed. The parameters were measured on 41 patients and 11 controls. The indices were extracted using Matlab-R14 software and the statistical analysis was performed using Spss-13. Results: Although there were significant differences in both the viscous and elastic indices between the pair groups and also among the UPDRS groups, but better correlations of the viscous ones and UPDRS were found. The range of motion by itself has no good correlation with the level of the disease. Discussion and Conclusion: Based on the obtained results, it can be inferred that using viscous indices of rigidity may have an advantage over the elastic ones for the evaluation of Parkinson’s disease. Upon conducting more trials and also considering the sub indices in different parts of the range of motion, the method used here may become a standard objective method for the evaluation of Parkinson's disease.

Introduction: Based on the invasive studies it has been shown that factors such as age, the progress of eye disorders, lens fibers compression and the biochemical changes of ocular matrix alter the physical characteristics and elastic properties of eye. In this study, a noninvasive method of estimating human eye elasticityis proposed and its relation with age and gender is evaluated using ultrasound images. Materials and Methods: To estimate eye elasticity, an especial loading system was designed and an external stress of 2614 ± 146 Pa which is less than the intraocular pressure of eye was applied to 20 eyes in an in vivo study. The pressure was measured using digital force gauge. The ultrasound images of B-mode were acquired prior to and post applying the stress. For the offline study throughout the loading process, the ultrasound images were saved as multi-frames into the computer by video grabber board. Monitoring, saving and further study of images were provided for the extraction of eye axial length and posterior wall thickness (PWT). The elasticity was estimated by measuring the relative changes of the axial length of eye, the posterior wall thickness and the applied stress. The statistical correlation of elastic modulus was analyzed based on age and gender. Results: The elastic modulus of the eye and the posterior wall thickness was estimated to be 51,777 ± 27304 and 14603 ± 4636 Pa, respectively. The obtained results indicated that there was no significant difference between the elastic parameters of the eye and the posterior wall thickness based on gender in both male and female group. The correlation analysis of the elastic parameter showed that there was significant difference between the eye and the posterior wall thickness based on age with a 95% confidence interval. Discussion and Conclusion: Based on the results obtained in this study the ultrasonic instruments might be used to estimate the hardness of eye lesions as well as eye posterior wall thickness.

Introduction: Heavy ions are nucleus of elements of iron, argon, carbon and neon that all carry positive electrical charges. For these particles to be useful in radiotherapy they need to accelerated to high energy by more than thousand mega volts. Also the cosmic environment is considered to be a complicated mixture of highly energetic photons and heavy ions such as iron. Therefore, the health risks to astronauts during long mission should be considered. Materials and Methods: The induction of interphase death was tested on Chinese hamster ovary cells by exposing them to accelerated heavy ions (carbon, neon, argon and iron) of 10-2000 linear energy transfers (LETs). The fraction of cells that underwent interphase death was determined by observing individual cells with time-lapse photography (direct method) as well as by the indirect method of counting cells undergoing interphase death made visible by the addition of caffeine (indirect method). Results: The interphase death due to the exposure to X- rays is increased linearly as the dose exceeds the threshold dose of 10 Gy. Whereas the interphase death increases at a higher rate due to the exposure to high LET heavy ions and no threshold dose was observed. The range of LET values corresponding to the maximum RBE for the interphase death is 120-230 keV/µm. The probability of inducing the interphase death by a single heavy ion traversing through the nucleus is about 0.04-0.08. Discussion and Conclusion: The relative biological effectiveness (RBE) of heavy ions as compared to X- rays as determined at the 50% level of induction is increased with LET. It reached a maximum value at a LET of approximately 230 keV/µm and then decreased with further increase in LET. The range of LET values corresponding to the maximum RBE appears to be narrower for interphase death than for reproductive death.

Introduction: Amajor problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. Laser mass spectrometry is a valuable tool for extracting the proteomic patterns from biological samples. A major challenge in extracting such patterns is the optimum selection of feature subset from mass spectrum data. Materials and Methods: In this research, the data corresponding to proteomic patterns of serum from patients with ovarian cancer was analyzed in two independent groups. Using a mathematical model, the baseline and electrical noises were eliminated in the preprocessing stage with subsequent normalization of mass spectra. The proposed method uses a hybrid algorithm based on a statistical test and Bhattacharyya distance measure. Using the final prediction error criteria, the best feature subset was selected from 15154 data points while maintaining the resolution and the valuable information. The selected feature subset was then used for the detection of biomarkers within the mass spectrum. Results: Using the method of k-fold cross validation, the samples under study were divided into two sets called the learning and test. Using the least threshold value, the points having significance difference (p-value < 0.05) were selected. The best subset was then extracted from the remaining points such that it would have the maximum information content. By doing so, the number of input variables was reduced from 15154 to 80 points. In the next step, 16 and 6 biomarkers were selected for the two independent dataset. The obtained results show accuracy, specificity as well as sensitivity of 100%. Discussion and Conclusion: To diagnose a disease in medicine is an example of pattern recognition in engineering and physical science. In this paper, a filter approach is introduced for feature subset selection which extracts appropriate features in the input space by using the combination of statistical method and distance measure based on information criteria. The result of this study emphasizes that the use of combination approach in feature extraction and selection in high dimensional data can appropriately separate the pattern classes in addition to maintaining the information content.